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Record W3208099436 · doi:10.1038/s41379-021-00949-w

Gastrointestinal stromal tumors (GISTs) arising in uncommon locations: clinicopathologic features and risk assessment of esophageal, colonic, and appendiceal GISTs

2021· article· en· W3208099436 on OpenAlexaff
Shaomin Hu, Lindsay Alpert, Justin Cates, Raul S. González, Rondell P. Graham, John R. Goldblum, Ahmed Bakhshwin, Sindhu Shetty, Hanlin L. Wang, Trang Lollie, Changqing Ma, Dipti M. Karamchandani, Fengming Chen, Rhonda K. Yantiss, Erika Hissong, Deyali Chatterjee, Shefali Chopra, Wei Chen, Jennifer Vazzano, Wei‐Lien Wang, Di Ai, Jingmei Lin, Lan Zheng, Jessica L. Davis, Brian Brinkerhoff, Amanda Breitbarth, Michelle Yang, Sepideh Madahian, Nicole C. Panarelli, Kevin Kuan, Jonathan Pomper, Teri A. Longacre, Shyam S. Raghavan, Joseph Misdraji, Min Cui, Zhaohai Yang, Deepika Savant, Noam Harpaz, Xiuxu Chen, Murray B. Resnick, Elizabeth Y. Wu, David S. Klimstra, Jinru Shia, Monika Vyas, Sanjay Kakar, Won‐Tak Choi, Marie E. Robert, Hongjie Li, Michael J. Lee, Ian A. Clark, Yongchao Li, Wenqing Cao, Qing Chang, Mary P. Bronner, Zachary M. Dong, Wei Zhang, Darya Buehler, Paul E. Swanson, José G. Mantilla, Andrew M. Bellizzi, Michael Feely, Harry S. Cooper, Rajeswari Nagarathinam, Rish K. Pai, Suntrea T.G. Hammer, Mojgan Hosseini, Jingjing Hu, Maria Westerhoff, Jerome Cheng, Diana Agostini‐Vulaj, Gregory Y. Lauwers, Masoumeh Ghayouri, Maryam Kherad Pezhouh, Jianying Zeng, Rong Xia, Tao Zhang, Zu‐Hua Gao, Nadine Demko, Hannah H. Chen, Sanhong Yu, John Hart

Bibliographic record

VenueModern Pathology · 2021
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal Tumor Research and Treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsGiSTRectumEsophagusMitotic indexGastroenterologyMedicineStomachPathologyInternal medicineMitosisStromal cellBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.340
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations29
Published2021
Admission routes1
Has abstractno

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